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1 Theory of Computation For Computer Science & Information Technology By

2 Syllabus Syllabus for Theory of Computation Regular Expressions and Finite Automata, Context-Free Grammar s and Push-Down Automata, Regular and Context-Free Languages, Pumping Lemma, Turing Machines and Undecidability. Analysis of GATE Papers Year Percentage of Marks Over All Percentage % : , Copyright reserved. Web:

3 Contents Contents Chapters Page No. #. Introduction/Preliminaries 3 Introduction Relations 2 3 #2. Finite Automata 4 22 Finite Automata 4 0 Construction of DFA from NFA (Sub Set Construction) 0 Epsilon( )-Closures 2 3 Eliminating -Transitions (Construction of DFA from -NFA) 4 5 Assignment 6 8 Assignment Answer keys & Explanations #3. Regular Expression Definitions Languages Associated with Regular Expressions Algebraic Laws for Regular Expressions Converting Regular Expression to Automata ( -NFA) Construction of Regular Expression from Finite Automata 28 3 Ordering the Elimination of States 3 33 Finite Automata with Output Regular Grammar Myhill-Nerode Theorem Pumping Lemma for Regular Languages Finding (in) Distinguishable States Assignment Answer keys & Explanations #4. Context Free Grammar Introduction Context Free Language Leftmost and Rightmost Derivations Ambiguity Simplification of Context Free Grammar 65 Elimination of Useless Symbols Normal Forms : , Copyright reserved. Web: i

4 Pushdown Automata Definition of PDA NPDA Properties of Context Free Language Decision Algorithms for CFL s Non-Context Free Language Pumping Lemma for CFL s 89 9 Membership Algorithm for Context-Free Grammar 9 92 Assignment Assignment Answer keys & Explanations Contents #5. Turing Machines 0 34 Introduction 0 03 Modification of Turing Machine Two-Pushdown Stack Machine Counter Machine 08 0 Multiple Tracks 0 6 Universal Turing Machine 6 7 Context Sensitive Grammar 7 8 Linear Bounded Automata 8 9 Hierarchy of Formal Languages (Chomsky Hierarchy) 9 20 Undecidability 20 2 Universal Language 2 27 The Classes P & NP Assignment 30 3 Assignment Answer Keys & Explanations Module Test Test Questions 35 4 Answer Keys & Explanations 4 45 Reference Books 46 : , Copyright reserved. Web: ii

5 CHAPTER "Yesterday I dared to struggle. Today I dare to win."..bernadette Devlin Introduction/Preliminaries Learning Objectives After reading this chapter, you will know:. Relations Introduction String: A string is a finite sequence of symbols put together. E.g. bbac the length of this string is '4'. Note: The length of empty string denoted by, is the string consisting of zero symbols. Thus = 0. Alphabet: An alphabet is a finite set of symbols. E.g.: {a, b, 0,, B} Formal language: A formal language is a set of strings made up of symbols taken from some alphabet. E.g.: The language consisting of all strings formed by the alphabet {0, } Note:. The empty set,, is a formal language. The cardinality (size) of this language is zero. 2. The set consisting of empty string, { } is a formal language. The cardinality (size) of this language is one. Set: A set is a collection of objects (members of the set) without repetition. i. Finite Set: A set which contains finite number of elements is said to be finite set. E.g.: {, 2, 3, 4} ii. Countably Infinite Set: Sets that can be placed in one-to-one correspondence with the integers are said to be countably infinite or countable or denumerable. E.g.: The set of the finite-length strings from an alphabet are countably infinite, (if : {0, } Then : {0, 0,, 0, 0...} i.e., all possible strings with 0 and'') iii. Uncountable Set: Sets that can't be placed in one-to-one correspondence with the integers are said to be uncountable sets. E.g.: The set of real numbers. : , Copyright reserved. Web:

6 Introduction/Preliminaries Relations A (binary) relation is a set of ordered tuples. The first component of each tuple is chosen from a set called the domain and the second component of each pair is chosen from a (possibly different) set called the range. E.g. Let A {, 2, 3, 4} be a set. A relation R, on 'A' can be defined as R: {(, 2), (, 3), (, ), (2, 3)} Properties of Relations We say a relation R on set S is. Reflexive: If ara for all a in S 2. Irreflexive: If ara is false for all a is S 3. Transitive: If arb and brc implies arc 4. Symmetric: If arb implies bra 5. Asymmetric: If arb implies that bra is false 6. Anti-symmetric: If arb and bra implies a = b Equivalence Relation: A relation is said to be equivalence relation if it satisfies the following properties.. Reflexive 2. Symmetric 3. Transitive An important property of equivalence relation 'R' on set 'S' is that R partitions 'S' into disjoint nonempty equivalence classes (may be infinite) i.e., S =..., where for each i and j, with i j. = 2. For each 'a' and 'b' in S jarb is true. 3. For each 'a' in i and 'b' in Sj, arb is false. The 's are called equivalence classes. E.g.: The relation 'R' on people defined by prq if and only if 'p' and 'q' were born at the same hour of the same day of some year: The number of equivalence classes are: 24 (no. of hours) 7 (no. of days in a week). Closure of Relations: Suppose P is a set of properties of relations. The P-closure of a relation R is the smallest relation R that includes all the pairs of R and possesses the properties in P. E.g.: Let R = {(, 2), (2, 3), (3, )} be a relation on set {, 2, 3} i. The reflexive-transitive closure of R is denoted by R* is Added by Reflexivity (, 2), (2, 3), (3, ), (, ), (2, 2), (3, 3), (, 3), (2, ), (3, 2) Added by Transitivity : , Copyright reserved. Web: 2

7 Introduction/Preliminaries ii. The symmetric closure of R is {(, 2), (2, 3), (3, ), (2, ), (3, 2), (, 3)} Term Definition prefix of s A string obtained by removing zero or more trailing symbols of string s; e.g., ban is a prefix of banana. suffix of s A string formed by deleting zero or more of the leading symbols of s; e.g., nana is a suffix of banana. substring of s A string obtained by deleting a prefix and a suffix from s; e.g., nan is a substring of banana. Every prefix and every suffix of s is a substring of s, but not every substring of s is a prefix or a suffix of s. For every string s, both s and are prefixes, suffixes, and substrings of s. proper prefix, suffix, or Any nonempty string x that is, respectively, a prefix, suffix, or substring of s substring of s such that s x. subsequence of s Any string formed by deleting zero or more not necessarily contiguous symbols from s; e.g., baaa is a subsequence of banana. : , Copyright reserved. Web: 3

8 CHAPTER 2 "Shoot for the moon. Even if you miss, you will land among the stars." Les Brown Finite Automata Learning Objectives After reading this chapter, you will know:. Finite Automata 2. Construction of DFA from NFA (Sub Set Construction) 3. Epsilon( )-Closures 4. Eliminating -Transitions (Construction of DFA from -NFA) Finite Automata A finite automaton involves states and transitions among states in response to inputs. They are useful for building several different kinds of software, including the lexical analysis component of a complier and systems for verifying the correctness of circuits or protocols. They also serve as the control unit in many physical systems including: vending machines, elevators, automatic traffic signals, computer microprocessors, and network protocol stacks. Deterministic Finite Automata A DFA captures the basic elements of an abstract machine: it reads in a string, and depending on the input and the way the machine was designed, it outputs either true or false. A DFA is always in one of N states, which we usually name 0 through N-. Each state is labeled true or false. The DFA begins in a designated state called the start state. As the input characters are read in one at a time, the DFA changes from one state to another in a pre-specified way. The new state is completely determined by the current state and the character just read in. When the input is exhausted, the DFA outputs true or false according to the label of the state it is currently in. A Deterministic finite automaton is represented by a Quintuple (5-tuple): (Q,, δ,, F) Where, Q: Finite set of states : Finite set of input symbols called the alphabet. δ: Q X Q (δ is a transition function from Q X to Q) : A start state, one of the states in Q F: A set of final states, such that F Q : , Copyright reserved. Web: 4

9 Induction Finite Automata Suppose is a string of the form a that is a is the last symbol of, and is the string consisting of all but the last symbol. For example is broken into and a. Then δ (, ) δ(δ (, ), a) Now, to compute δ (, ) first compute δ (, )the state that the automaton is in after processing all but the last symbol of. Suppose this state is p; that is, δ (, ). Then δ (, )is what we get by making a transition from state p on input a. That is, δ (, ) = δ (, a) E.g.: Let us design a DFA to accept the language L = { has both an even number of 0's and an even number of 's} It should not be surprising that the job of the states of this DFA is to count both the number of 0's and the number of 's, but count them modulo 2. That is, the state is used to remember whether the number of 0's seen so far is even or odd, and also to remember whether the number of 's seen so far is even or odd. There are thus four states, which can be given the following interpretations: : Both the number of 0's seen so far and the number of 's seen so far are even. : The number of 0's seen so far is even, but the number of 's seen so far is odd. : The number of 's seen so far is even, but the number of 0's seen so far is odd. : Both the number of 0's seen so far and the number of 's seen so far are odd. State is both the start state and the alone the accepting state. It is the start state, because before reading any inputs, the numbers of 0's and 's seen so far are both zero, and zero is even. It is the only accepting state, because it describes exactly the condition for a sequence of 0's and 's to be in language L. We now know almost how to specify the DFA for language L. It is, A ({,,, }, {, }, δ,, { }) Deterministic Finite Automata Start Transition Diagram for the DFA Where the transition function S is described by the transition diagram notice that, how each input 0 causes the state to cross the horizontal, dashed line. Thus, after seeing an even number of 0's we are always above the line, in state or while after seeing an odd number of 0's we are always below the line, in state or. Likewise, every causes the state to cross the vertical, dashed line. Thus, after seeing an even number of 's. We are always to the left, in state or. While after : , Copyright reserved. Web: 5

10 Finite Automata seeing an odd number of 's we are to the right, in state or. These observations are an informal proof that the four states have the interpretations attributed to them. However, one could prove the correctness of our claims about the states formally, by a mutual induction with respect to example. We can also represent this DFA by a transition table shown below. However, we are not just concerned with the design of this DFA; we want to use it to illustrate the construction of δ from its transition function δ. Suppose the input is 00. Since this string has even numbers of 0's and 's both, we expect it is in the language. Thus, we expect that δ (, 00) = since is the only accepting state. Let us now verify that claim. Transition Table for the DFA The check involves computing δ (, w) for each prefix w of 00, starting at and going in increasing size. The summary of this calculation is: δ (, ) δ (, ) δ(δ (, ), ) δ(, ) δ (, ) δ(δ (, ), ) δ(, ) δ (, ) δ(δ (, ), ) δ(, ) δ (, ) δ(δ (, ), ) δ(, ) δ (, ) δ(δ (, ), ) δ(, ) δ (, ) δ(δ (, ), ) δ(, ) Acceptance by an Automata A string is said to be acce ted by a finite automaton M (Q,, δ,, F) if δ (, x) = for some in F. The language accepted by M, designated L (M), is the set {x δ (, x) is in F}. A language is a regular set (or just regular) if it is the set accepted by some automaton. There are two preferred notations for describing Automata. Transition diagram 2. Transition table : , Copyright reserved. Web: 6

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